Overview

Dataset statistics

Number of variables20
Number of observations860
Missing cells0
Missing cells (%)0.0%
Duplicate rows4
Duplicate rows (%)0.5%
Total size in memory447.4 KiB
Average record size in memory532.7 B

Variable types

Categorical9
Numeric11

Alerts

Dataset has 4 (0.5%) duplicate rowsDuplicates
cpu_cores is highly overall correlated with cpu_threads and 6 other fieldsHigh correlation
cpu_threads is highly overall correlated with cpu_cores and 5 other fieldsHigh correlation
display_size is highly overall correlated with gpu_vramHigh correlation
gpu_brand is highly overall correlated with gpu_tier and 4 other fieldsHigh correlation
gpu_tier is highly overall correlated with gpu_brand and 2 other fieldsHigh correlation
gpu_vram is highly overall correlated with cpu_threads and 4 other fieldsHigh correlation
price is highly overall correlated with cpu_cores and 5 other fieldsHigh correlation
proc_brand is highly overall correlated with cpu_cores and 4 other fieldsHigh correlation
proc_gen is highly overall correlated with cpu_cores and 3 other fieldsHigh correlation
proc_tier is highly overall correlated with cpu_cores and 4 other fieldsHigh correlation
ram is highly overall correlated with cpu_cores and 3 other fieldsHigh correlation
resolution_height is highly overall correlated with cpu_cores and 3 other fieldsHigh correlation
resolution_width is highly overall correlated with resolution_heightHigh correlation
rom is highly overall correlated with price and 1 other fieldsHigh correlation
rom_type is highly imbalanced (84.7%)Imbalance
warranty is highly imbalanced (75.1%)Imbalance
os_family is highly imbalanced (87.2%)Imbalance
proc_gen has 39 (4.5%) zerosZeros
gpu_vram has 556 (64.7%) zerosZeros

Reproduction

Analysis started2026-01-13 18:58:48.027570
Analysis finished2026-01-13 18:59:05.175434
Duration17.15 seconds
Software versionydata-profiling vv4.18.0
Download configurationconfig.json

Variables

brand
Categorical

Distinct9
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size51.3 KiB
HP
186 
Lenovo
168 
Asus
154 
Dell
99 
Acer
80 
Other values (4)
173 

Length

Max length7
Median length6
Mean length4.1104651
Min length2

Characters and Unicode

Total characters3535
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHP
2nd rowHP
3rd rowAcer
4th rowLenovo
5th rowAcer

Common Values

ValueCountFrequency (%)
HP186
21.6%
Lenovo168
19.5%
Asus154
17.9%
Dell99
11.5%
Acer80
9.3%
Other66
 
7.7%
MSI64
 
7.4%
Samsung28
 
3.3%
Infinix15
 
1.7%

Length

2026-01-14T00:59:05.276796image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-14T00:59:05.440886image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
hp186
21.6%
lenovo168
19.5%
asus154
17.9%
dell99
11.5%
acer80
9.3%
other66
 
7.7%
msi64
 
7.4%
samsung28
 
3.3%
infinix15
 
1.7%

Most occurring characters

ValueCountFrequency (%)
e413
 
11.7%
o336
 
9.5%
s336
 
9.5%
A234
 
6.6%
n226
 
6.4%
l198
 
5.6%
P186
 
5.3%
H186
 
5.3%
u182
 
5.1%
L168
 
4.8%
Other values (16)1070
30.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)3535
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e413
 
11.7%
o336
 
9.5%
s336
 
9.5%
A234
 
6.6%
n226
 
6.4%
l198
 
5.6%
P186
 
5.3%
H186
 
5.3%
u182
 
5.1%
L168
 
4.8%
Other values (16)1070
30.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)3535
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e413
 
11.7%
o336
 
9.5%
s336
 
9.5%
A234
 
6.6%
n226
 
6.4%
l198
 
5.6%
P186
 
5.3%
H186
 
5.3%
u182
 
5.1%
L168
 
4.8%
Other values (16)1070
30.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)3535
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e413
 
11.7%
o336
 
9.5%
s336
 
9.5%
A234
 
6.6%
n226
 
6.4%
l198
 
5.6%
P186
 
5.3%
H186
 
5.3%
u182
 
5.1%
L168
 
4.8%
Other values (16)1070
30.3%

price
Real number (ℝ)

High correlation 

Distinct445
Distinct (%)51.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean77588.441
Minimum10990
Maximum450039
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size13.4 KiB
2026-01-14T00:59:05.631898image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum10990
5-th percentile27845.45
Q144142.5
median60990
Q389574.75
95-th percentile187553.1
Maximum450039
Range439049
Interquartile range (IQR)45432.25

Descriptive statistics

Standard deviation58138.133
Coefficient of variation (CV)0.74931436
Kurtosis10.974429
Mean77588.441
Median Absolute Deviation (MAD)21000
Skewness2.8820022
Sum66726059
Variance3.3800425 × 109
MonotonicityNot monotonic
2026-01-14T00:59:06.084295image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4999016
 
1.9%
3799013
 
1.5%
5999012
 
1.4%
5499011
 
1.3%
7999011
 
1.3%
6299011
 
1.3%
6499011
 
1.3%
4799011
 
1.3%
5799011
 
1.3%
7499011
 
1.3%
Other values (435)742
86.3%
ValueCountFrequency (%)
109902
0.2%
129901
0.1%
139901
0.1%
144901
0.1%
149901
0.1%
159902
0.2%
169901
0.1%
179901
0.1%
189902
0.2%
198501
0.1%
ValueCountFrequency (%)
4500391
0.1%
4299901
0.1%
4200001
0.1%
4150001
0.1%
3999991
0.1%
3909141
0.1%
3629991
0.1%
3449901
0.1%
3399901
0.1%
3232901
0.1%

spec_rating
Real number (ℝ)

Distinct30
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean69.301953
Minimum60
Maximum89
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size13.4 KiB
2026-01-14T00:59:06.246146image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum60
5-th percentile60
Q166
median69.32
Q371
95-th percentile80
Maximum89
Range29
Interquartile range (IQR)5

Descriptive statistics

Standard deviation5.5159464
Coefficient of variation (CV)0.079592943
Kurtosis1.4080952
Mean69.301953
Median Absolute Deviation (MAD)2.32
Skewness0.86592619
Sum59599.68
Variance30.425665
MonotonicityNot monotonic
2026-01-14T00:59:06.418829image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
69.32274
31.9%
6047
 
5.5%
7144
 
5.1%
7043
 
5.0%
6242
 
4.9%
6739
 
4.5%
6537
 
4.3%
6437
 
4.3%
6637
 
4.3%
6935
 
4.1%
Other values (20)225
26.2%
ValueCountFrequency (%)
6047
5.5%
616
 
0.7%
6242
4.9%
6332
3.7%
6437
4.3%
6537
4.3%
6637
4.3%
6739
4.5%
686
 
0.7%
6935
4.1%
ValueCountFrequency (%)
894
 
0.5%
884
 
0.5%
864
 
0.5%
856
0.7%
844
 
0.5%
839
1.0%
825
0.6%
812
 
0.2%
8012
1.4%
7910
1.2%

ram
Real number (ℝ)

High correlation 

Distinct6
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.15814
Minimum4
Maximum64
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size13.4 KiB
2026-01-14T00:59:06.554482image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile8
Q18
median16
Q316
95-th percentile16
Maximum64
Range60
Interquartile range (IQR)8

Descriptive statistics

Standard deviation6.1776658
Coefficient of variation (CV)0.46949386
Kurtosis11.743289
Mean13.15814
Median Absolute Deviation (MAD)0
Skewness2.2205975
Sum11316
Variance38.163554
MonotonicityNot monotonic
2026-01-14T00:59:06.669132image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
16442
51.4%
8357
41.5%
3236
 
4.2%
421
 
2.4%
122
 
0.2%
642
 
0.2%
ValueCountFrequency (%)
421
 
2.4%
8357
41.5%
122
 
0.2%
16442
51.4%
3236
 
4.2%
642
 
0.2%
ValueCountFrequency (%)
642
 
0.2%
3236
 
4.2%
16442
51.4%
122
 
0.2%
8357
41.5%
421
 
2.4%

ram_type
Categorical

Distinct7
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size51.7 KiB
DDR4
490 
DDR5
155 
LPDDR5
143 
LPDDR4X
54 
LPDDR4
 
13
Other values (2)
 
5

Length

Max length7
Median length4
Mean length4.5616279
Min length4

Characters and Unicode

Total characters3923
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDDR4
2nd rowDDR4
3rd rowDDR4
4th rowLPDDR5
5th rowDDR4

Common Values

ValueCountFrequency (%)
DDR4490
57.0%
DDR5155
 
18.0%
LPDDR5143
 
16.6%
LPDDR4X54
 
6.3%
LPDDR413
 
1.5%
LPDDR5X3
 
0.3%
DDR32
 
0.2%

Length

2026-01-14T00:59:06.817310image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-14T00:59:06.976227image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
ddr4490
57.0%
ddr5155
 
18.0%
lpddr5143
 
16.6%
lpddr4x54
 
6.3%
lpddr413
 
1.5%
lpddr5x3
 
0.3%
ddr32
 
0.2%

Most occurring characters

ValueCountFrequency (%)
D1720
43.8%
R860
21.9%
4557
 
14.2%
5301
 
7.7%
L213
 
5.4%
P213
 
5.4%
X57
 
1.5%
32
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)3923
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
D1720
43.8%
R860
21.9%
4557
 
14.2%
5301
 
7.7%
L213
 
5.4%
P213
 
5.4%
X57
 
1.5%
32
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)3923
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
D1720
43.8%
R860
21.9%
4557
 
14.2%
5301
 
7.7%
L213
 
5.4%
P213
 
5.4%
X57
 
1.5%
32
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)3923
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
D1720
43.8%
R860
21.9%
4557
 
14.2%
5301
 
7.7%
L213
 
5.4%
P213
 
5.4%
X57
 
1.5%
32
 
0.1%

rom
Real number (ℝ)

High correlation 

Distinct7
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean612.94884
Minimum32
Maximum2048
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size13.4 KiB
2026-01-14T00:59:07.121263image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum32
5-th percentile256
Q1512
median512
Q3512
95-th percentile1024
Maximum2048
Range2016
Interquartile range (IQR)0

Descriptive statistics

Standard deviation267.9458
Coefficient of variation (CV)0.4371422
Kurtosis6.2621515
Mean612.94884
Median Absolute Deviation (MAD)0
Skewness1.9065562
Sum527136
Variance71794.954
MonotonicityNot monotonic
2026-01-14T00:59:07.261585image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
512622
72.3%
1024177
 
20.6%
25636
 
4.2%
12812
 
1.4%
20488
 
0.9%
644
 
0.5%
321
 
0.1%
ValueCountFrequency (%)
321
 
0.1%
644
 
0.5%
12812
 
1.4%
25636
 
4.2%
512622
72.3%
1024177
 
20.6%
20488
 
0.9%
ValueCountFrequency (%)
20488
 
0.9%
1024177
 
20.6%
512622
72.3%
25636
 
4.2%
12812
 
1.4%
644
 
0.5%
321
 
0.1%

rom_type
Categorical

Imbalance 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size50.5 KiB
SSD
841 
Hard-Disk
 
19

Length

Max length9
Median length3
Mean length3.1325581
Min length3

Characters and Unicode

Total characters2694
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSSD
2nd rowSSD
3rd rowSSD
4th rowSSD
5th rowSSD

Common Values

ValueCountFrequency (%)
SSD841
97.8%
Hard-Disk19
 
2.2%

Length

2026-01-14T00:59:07.460829image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-14T00:59:07.587317image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
ssd841
97.8%
hard-disk19
 
2.2%

Most occurring characters

ValueCountFrequency (%)
S1682
62.4%
D860
31.9%
H19
 
0.7%
a19
 
0.7%
r19
 
0.7%
d19
 
0.7%
-19
 
0.7%
i19
 
0.7%
s19
 
0.7%
k19
 
0.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)2694
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S1682
62.4%
D860
31.9%
H19
 
0.7%
a19
 
0.7%
r19
 
0.7%
d19
 
0.7%
-19
 
0.7%
i19
 
0.7%
s19
 
0.7%
k19
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)2694
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S1682
62.4%
D860
31.9%
H19
 
0.7%
a19
 
0.7%
r19
 
0.7%
d19
 
0.7%
-19
 
0.7%
i19
 
0.7%
s19
 
0.7%
k19
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)2694
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S1682
62.4%
D860
31.9%
H19
 
0.7%
a19
 
0.7%
r19
 
0.7%
d19
 
0.7%
-19
 
0.7%
i19
 
0.7%
s19
 
0.7%
k19
 
0.7%

display_size
Real number (ℝ)

High correlation 

Distinct14
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.186116
Minimum11.6
Maximum18
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size13.4 KiB
2026-01-14T00:59:07.701249image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum11.6
5-th percentile14
Q114
median15.6
Q315.6
95-th percentile16
Maximum18
Range6.4
Interquartile range (IQR)1.6

Descriptive statistics

Standard deviation0.92242394
Coefficient of variation (CV)0.060741267
Kurtosis0.39060902
Mean15.186116
Median Absolute Deviation (MAD)0
Skewness-0.74884321
Sum13060.06
Variance0.85086592
MonotonicityNot monotonic
2026-01-14T00:59:07.840283image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
15.6454
52.8%
14212
24.7%
16106
 
12.3%
13.325
 
2.9%
16.121
 
2.4%
17.314
 
1.6%
14.17
 
0.8%
11.65
 
0.6%
175
 
0.6%
13.44
 
0.5%
Other values (4)7
 
0.8%
ValueCountFrequency (%)
11.65
 
0.6%
13.325
 
2.9%
13.44
 
0.5%
13.52
 
0.2%
14212
24.7%
14.17
 
0.8%
153
 
0.3%
15.561
 
0.1%
15.6454
52.8%
16106
 
12.3%
ValueCountFrequency (%)
181
 
0.1%
17.314
 
1.6%
175
 
0.6%
16.121
 
2.4%
16106
 
12.3%
15.6454
52.8%
15.561
 
0.1%
153
 
0.3%
14.17
 
0.8%
14212
24.7%

resolution_width
Real number (ℝ)

High correlation 

Distinct16
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2023.2488
Minimum1080
Maximum3840
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size13.4 KiB
2026-01-14T00:59:07.966515image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1080
5-th percentile1366
Q11920
median1920
Q31920
95-th percentile2880
Maximum3840
Range2760
Interquartile range (IQR)0

Descriptive statistics

Standard deviation414.94223
Coefficient of variation (CV)0.20508709
Kurtosis5.8531048
Mean2023.2488
Median Absolute Deviation (MAD)0
Skewness1.950389
Sum1739994
Variance172177.05
MonotonicityNot monotonic
2026-01-14T00:59:08.095266image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
1920667
77.6%
256062
 
7.2%
136639
 
4.5%
288038
 
4.4%
384014
 
1.6%
320011
 
1.3%
10808
 
0.9%
12004
 
0.5%
16004
 
0.5%
21603
 
0.3%
Other values (6)10
 
1.2%
ValueCountFrequency (%)
10808
 
0.9%
12004
 
0.5%
12802
 
0.2%
136639
 
4.5%
14401
 
0.1%
16004
 
0.5%
1920667
77.6%
21603
 
0.3%
22402
 
0.2%
22561
 
0.1%
ValueCountFrequency (%)
384014
 
1.6%
34562
 
0.2%
320011
 
1.3%
288038
 
4.4%
256062
 
7.2%
24962
 
0.2%
22561
 
0.1%
22402
 
0.2%
21603
 
0.3%
1920667
77.6%

resolution_height
Real number (ℝ)

High correlation 

Distinct19
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1207.7767
Minimum768
Maximum3456
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size13.4 KiB
2026-01-14T00:59:08.232513image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum768
5-th percentile1080
Q11080
median1080
Q31200
95-th percentile1920
Maximum3456
Range2688
Interquartile range (IQR)120

Descriptive statistics

Standard deviation316.12029
Coefficient of variation (CV)0.26173736
Kurtosis6.7750486
Mean1207.7767
Median Absolute Deviation (MAD)0
Skewness2.3185066
Sum1038688
Variance99932.039
MonotonicityNot monotonic
2026-01-14T00:59:08.380496image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
1080566
65.8%
120099
 
11.5%
160047
 
5.5%
76839
 
4.5%
180034
 
4.0%
144015
 
1.7%
192012
 
1.4%
240011
 
1.3%
20009
 
1.0%
21606
 
0.7%
Other values (9)22
 
2.6%
ValueCountFrequency (%)
76839
 
4.5%
10242
 
0.2%
1080566
65.8%
120099
 
11.5%
12801
 
0.1%
14002
 
0.2%
144015
 
1.7%
15041
 
0.1%
160047
 
5.5%
16206
 
0.7%
ValueCountFrequency (%)
34561
 
0.1%
25605
 
0.6%
240011
 
1.3%
21606
 
0.7%
20009
 
1.0%
192012
 
1.4%
180034
4.0%
16642
 
0.2%
16602
 
0.2%
16206
 
0.7%

warranty
Categorical

Imbalance 

Distinct4
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size48.7 KiB
1
787 
2
 
58
3
 
9
0
 
6

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters860
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1787
91.5%
258
 
6.7%
39
 
1.0%
06
 
0.7%

Length

2026-01-14T00:59:08.542910image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-14T00:59:08.703181image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
1787
91.5%
258
 
6.7%
39
 
1.0%
06
 
0.7%

Most occurring characters

ValueCountFrequency (%)
1787
91.5%
258
 
6.7%
39
 
1.0%
06
 
0.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)860
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1787
91.5%
258
 
6.7%
39
 
1.0%
06
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)860
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1787
91.5%
258
 
6.7%
39
 
1.0%
06
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)860
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1787
91.5%
258
 
6.7%
39
 
1.0%
06
 
0.7%

proc_brand
Categorical

High correlation 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size51.6 KiB
Intel
595 
AMD
265 

Length

Max length5
Median length5
Mean length4.3837209
Min length3

Characters and Unicode

Total characters3770
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAMD
2nd rowIntel
3rd rowIntel
4th rowIntel
5th rowIntel

Common Values

ValueCountFrequency (%)
Intel595
69.2%
AMD265
30.8%

Length

2026-01-14T00:59:08.862068image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-14T00:59:09.008822image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
intel595
69.2%
amd265
30.8%

Most occurring characters

ValueCountFrequency (%)
I595
15.8%
n595
15.8%
t595
15.8%
e595
15.8%
l595
15.8%
A265
7.0%
M265
7.0%
D265
7.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)3770
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
I595
15.8%
n595
15.8%
t595
15.8%
e595
15.8%
l595
15.8%
A265
7.0%
M265
7.0%
D265
7.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)3770
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
I595
15.8%
n595
15.8%
t595
15.8%
e595
15.8%
l595
15.8%
A265
7.0%
M265
7.0%
D265
7.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)3770
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
I595
15.8%
n595
15.8%
t595
15.8%
e595
15.8%
l595
15.8%
A265
7.0%
M265
7.0%
D265
7.0%

proc_tier
Categorical

High correlation 

Distinct10
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size49.6 KiB
i5
287 
i7
137 
R5
128 
i3
105 
R7
87 
Other values (5)
116 

Length

Max length5
Median length2
Mean length2.0488372
Min length2

Characters and Unicode

Total characters1762
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rowR5
2nd rowi3
3rd rowi3
4th rowi5
5th rowi5

Common Values

ValueCountFrequency (%)
i5287
33.4%
i7137
15.9%
R5128
14.9%
i3105
 
12.2%
R787
 
10.1%
Low39
 
4.5%
R334
 
4.0%
i934
 
4.0%
R98
 
0.9%
Other1
 
0.1%

Length

2026-01-14T00:59:09.152810image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-14T00:59:09.320118image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
i5287
33.4%
i7137
15.9%
r5128
14.9%
i3105
 
12.2%
r787
 
10.1%
low39
 
4.5%
r334
 
4.0%
i934
 
4.0%
r98
 
0.9%
other1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
i563
32.0%
5415
23.6%
R257
14.6%
7224
 
12.7%
3139
 
7.9%
942
 
2.4%
L39
 
2.2%
o39
 
2.2%
w39
 
2.2%
O1
 
0.1%
Other values (4)4
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)1762
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i563
32.0%
5415
23.6%
R257
14.6%
7224
 
12.7%
3139
 
7.9%
942
 
2.4%
L39
 
2.2%
o39
 
2.2%
w39
 
2.2%
O1
 
0.1%
Other values (4)4
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1762
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i563
32.0%
5415
23.6%
R257
14.6%
7224
 
12.7%
3139
 
7.9%
942
 
2.4%
L39
 
2.2%
o39
 
2.2%
w39
 
2.2%
O1
 
0.1%
Other values (4)4
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1762
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i563
32.0%
5415
23.6%
R257
14.6%
7224
 
12.7%
3139
 
7.9%
942
 
2.4%
L39
 
2.2%
o39
 
2.2%
w39
 
2.2%
O1
 
0.1%
Other values (4)4
 
0.2%

proc_gen
Real number (ℝ)

High correlation  Zeros 

Distinct12
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.6604651
Minimum0
Maximum13
Zeros39
Zeros (%)4.5%
Negative0
Negative (%)0.0%
Memory size13.4 KiB
2026-01-14T00:59:09.510210image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q17
median11
Q312
95-th percentile13
Maximum13
Range13
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.5993881
Coefficient of variation (CV)0.37258952
Kurtosis0.15102742
Mean9.6604651
Median Absolute Deviation (MAD)2
Skewness-1.0498579
Sum8308
Variance12.955595
MonotonicityNot monotonic
2026-01-14T00:59:09.658452image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
12213
24.8%
13197
22.9%
11131
15.2%
7126
14.7%
5101
11.7%
039
 
4.5%
1018
 
2.1%
617
 
2.0%
312
 
1.4%
84
 
0.5%
Other values (2)2
 
0.2%
ValueCountFrequency (%)
039
 
4.5%
312
 
1.4%
41
 
0.1%
5101
11.7%
617
 
2.0%
7126
14.7%
84
 
0.5%
91
 
0.1%
1018
 
2.1%
11131
15.2%
ValueCountFrequency (%)
13197
22.9%
12213
24.8%
11131
15.2%
1018
 
2.1%
91
 
0.1%
84
 
0.5%
7126
14.7%
617
 
2.0%
5101
11.7%
41
 
0.1%

gpu_brand
Categorical

High correlation 

Distinct4
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size52.0 KiB
Intel
391 
NVIDIA
293 
AMD
173 
Other
 
3

Length

Max length6
Median length5
Mean length4.9383721
Min length3

Characters and Unicode

Total characters4247
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAMD
2nd rowIntel
3rd rowIntel
4th rowIntel
5th rowIntel

Common Values

ValueCountFrequency (%)
Intel391
45.5%
NVIDIA293
34.1%
AMD173
20.1%
Other3
 
0.3%

Length

2026-01-14T00:59:09.825598image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-14T00:59:09.970928image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
intel391
45.5%
nvidia293
34.1%
amd173
20.1%
other3
 
0.3%

Most occurring characters

ValueCountFrequency (%)
I977
23.0%
D466
11.0%
A466
11.0%
t394
9.3%
e394
9.3%
l391
9.2%
n391
9.2%
V293
 
6.9%
N293
 
6.9%
M173
 
4.1%
Other values (3)9
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)4247
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
I977
23.0%
D466
11.0%
A466
11.0%
t394
9.3%
e394
9.3%
l391
9.2%
n391
9.2%
V293
 
6.9%
N293
 
6.9%
M173
 
4.1%
Other values (3)9
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)4247
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
I977
23.0%
D466
11.0%
A466
11.0%
t394
9.3%
e394
9.3%
l391
9.2%
n391
9.2%
V293
 
6.9%
N293
 
6.9%
M173
 
4.1%
Other values (3)9
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)4247
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
I977
23.0%
D466
11.0%
A466
11.0%
t394
9.3%
e394
9.3%
l391
9.2%
n391
9.2%
V293
 
6.9%
N293
 
6.9%
M173
 
4.1%
Other values (3)9
 
0.2%

gpu_tier
Categorical

High correlation 

Distinct5
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size53.4 KiB
Integrated
390 
RTX
257 
Other
178 
GTX
 
28
MX
 
7

Length

Max length10
Median length5
Mean length6.5802326
Min length2

Characters and Unicode

Total characters5659
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOther
2nd rowIntegrated
3rd rowIntegrated
4th rowIntegrated
5th rowIntegrated

Common Values

ValueCountFrequency (%)
Integrated390
45.3%
RTX257
29.9%
Other178
20.7%
GTX28
 
3.3%
MX7
 
0.8%

Length

2026-01-14T00:59:10.136586image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-14T00:59:10.288653image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
integrated390
45.3%
rtx257
29.9%
other178
20.7%
gtx28
 
3.3%
mx7
 
0.8%

Most occurring characters

ValueCountFrequency (%)
t958
16.9%
e958
16.9%
r568
10.0%
n390
6.9%
I390
6.9%
g390
6.9%
a390
6.9%
d390
6.9%
X292
 
5.2%
T285
 
5.0%
Other values (5)648
11.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)5659
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t958
16.9%
e958
16.9%
r568
10.0%
n390
6.9%
I390
6.9%
g390
6.9%
a390
6.9%
d390
6.9%
X292
 
5.2%
T285
 
5.0%
Other values (5)648
11.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)5659
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t958
16.9%
e958
16.9%
r568
10.0%
n390
6.9%
I390
6.9%
g390
6.9%
a390
6.9%
d390
6.9%
X292
 
5.2%
T285
 
5.0%
Other values (5)648
11.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)5659
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t958
16.9%
e958
16.9%
r568
10.0%
n390
6.9%
I390
6.9%
g390
6.9%
a390
6.9%
d390
6.9%
X292
 
5.2%
T285
 
5.0%
Other values (5)648
11.5%

gpu_vram
Real number (ℝ)

High correlation  Zeros 

Distinct7
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0186047
Minimum0
Maximum16
Zeros556
Zeros (%)64.7%
Negative0
Negative (%)0.0%
Memory size13.4 KiB
2026-01-14T00:59:10.422825image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q34
95-th percentile8
Maximum16
Range16
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.109133
Coefficient of variation (CV)1.5402387
Kurtosis2.614076
Mean2.0186047
Median Absolute Deviation (MAD)0
Skewness1.583418
Sum1736
Variance9.6667082
MonotonicityNot monotonic
2026-01-14T00:59:10.563382image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0556
64.7%
4153
 
17.8%
666
 
7.7%
865
 
7.6%
167
 
0.8%
127
 
0.8%
26
 
0.7%
ValueCountFrequency (%)
0556
64.7%
26
 
0.7%
4153
 
17.8%
666
 
7.7%
865
 
7.6%
127
 
0.8%
167
 
0.8%
ValueCountFrequency (%)
167
 
0.8%
127
 
0.8%
865
 
7.6%
666
 
7.7%
4153
 
17.8%
26
 
0.7%
0556
64.7%

cpu_cores
Real number (ℝ)

High correlation 

Distinct10
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.8313953
Minimum2
Maximum24
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size13.4 KiB
2026-01-14T00:59:10.720098image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile2
Q14
median8
Q310
95-th percentile14
Maximum24
Range22
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.1315043
Coefficient of variation (CV)0.5275566
Kurtosis2.5413268
Mean7.8313953
Median Absolute Deviation (MAD)2
Skewness1.1026205
Sum6735
Variance17.069328
MonotonicityNot monotonic
2026-01-14T00:59:10.844592image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
6170
19.8%
10153
17.8%
8146
17.0%
4133
15.5%
290
10.5%
1283
9.7%
1451
 
5.9%
2416
 
1.9%
1611
 
1.3%
57
 
0.8%
ValueCountFrequency (%)
290
10.5%
4133
15.5%
57
 
0.8%
6170
19.8%
8146
17.0%
10153
17.8%
1283
9.7%
1451
 
5.9%
1611
 
1.3%
2416
 
1.9%
ValueCountFrequency (%)
2416
 
1.9%
1611
 
1.3%
1451
 
5.9%
1283
9.7%
10153
17.8%
8146
17.0%
6170
19.8%
57
 
0.8%
4133
15.5%
290
10.5%

cpu_threads
Real number (ℝ)

High correlation 

Distinct9
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.181395
Minimum2
Maximum32
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size13.4 KiB
2026-01-14T00:59:10.967272image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile4
Q18
median12
Q316
95-th percentile20
Maximum32
Range30
Interquartile range (IQR)8

Descriptive statistics

Standard deviation5.410689
Coefficient of variation (CV)0.44417646
Kurtosis2.1280597
Mean12.181395
Median Absolute Deviation (MAD)4
Skewness0.74706347
Sum10476
Variance29.275556
MonotonicityNot monotonic
2026-01-14T00:59:11.091879image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
12297
34.5%
16211
24.5%
8173
20.1%
459
 
6.9%
2051
 
5.9%
235
 
4.1%
3217
 
2.0%
2410
 
1.2%
67
 
0.8%
ValueCountFrequency (%)
235
 
4.1%
459
 
6.9%
67
 
0.8%
8173
20.1%
12297
34.5%
16211
24.5%
2051
 
5.9%
2410
 
1.2%
3217
 
2.0%
ValueCountFrequency (%)
3217
 
2.0%
2410
 
1.2%
2051
 
5.9%
16211
24.5%
12297
34.5%
8173
20.1%
67
 
0.8%
459
 
6.9%
235
 
4.1%

os_family
Categorical

Imbalance 

Distinct5
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size53.7 KiB
Windows
826 
DOS
 
20
ChromeOS
 
10
Linux
 
2
Mac
 
2

Length

Max length8
Median length7
Mean length6.9046512
Min length3

Characters and Unicode

Total characters5938
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWindows
2nd rowWindows
3rd rowWindows
4th rowWindows
5th rowWindows

Common Values

ValueCountFrequency (%)
Windows826
96.0%
DOS20
 
2.3%
ChromeOS10
 
1.2%
Linux2
 
0.2%
Mac2
 
0.2%

Length

2026-01-14T00:59:11.238423image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-14T00:59:11.376043image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
windows826
96.0%
dos20
 
2.3%
chromeos10
 
1.2%
linux2
 
0.2%
mac2
 
0.2%

Most occurring characters

ValueCountFrequency (%)
o836
14.1%
n828
13.9%
i828
13.9%
W826
13.9%
d826
13.9%
w826
13.9%
s826
13.9%
O30
 
0.5%
S30
 
0.5%
D20
 
0.3%
Other values (11)62
 
1.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)5938
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o836
14.1%
n828
13.9%
i828
13.9%
W826
13.9%
d826
13.9%
w826
13.9%
s826
13.9%
O30
 
0.5%
S30
 
0.5%
D20
 
0.3%
Other values (11)62
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)5938
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o836
14.1%
n828
13.9%
i828
13.9%
W826
13.9%
d826
13.9%
w826
13.9%
s826
13.9%
O30
 
0.5%
S30
 
0.5%
D20
 
0.3%
Other values (11)62
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)5938
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o836
14.1%
n828
13.9%
i828
13.9%
W826
13.9%
d826
13.9%
w826
13.9%
s826
13.9%
O30
 
0.5%
S30
 
0.5%
D20
 
0.3%
Other values (11)62
 
1.0%

Interactions

2026-01-14T00:59:03.223600image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2026-01-14T00:58:49.117643image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2026-01-14T00:58:50.489616image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2026-01-14T00:58:51.899140image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2026-01-14T00:58:53.277129image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2026-01-14T00:58:54.715874image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2026-01-14T00:58:56.139790image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2026-01-14T00:58:57.566288image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2026-01-14T00:58:58.971210image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2026-01-14T00:59:00.382124image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2026-01-14T00:59:01.848159image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2026-01-14T00:59:03.341918image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2026-01-14T00:58:49.236236image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2026-01-14T00:58:50.608954image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2026-01-14T00:58:52.018753image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2026-01-14T00:58:53.397780image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2026-01-14T00:58:54.832122image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2026-01-14T00:58:56.261240image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2026-01-14T00:58:57.688545image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2026-01-14T00:58:59.088759image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2026-01-14T00:59:00.510321image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2026-01-14T00:59:01.964164image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2026-01-14T00:59:03.471824image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2026-01-14T00:58:49.358864image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2026-01-14T00:58:50.742343image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2026-01-14T00:58:52.147843image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2026-01-14T00:58:53.528013image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2026-01-14T00:58:54.963127image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2026-01-14T00:58:56.399570image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2026-01-14T00:58:57.814836image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2026-01-14T00:58:59.216151image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2026-01-14T00:59:00.651647image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2026-01-14T00:59:02.088293image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2026-01-14T00:59:03.595232image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2026-01-14T00:58:49.473794image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2026-01-14T00:58:50.865701image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2026-01-14T00:58:52.265537image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2026-01-14T00:58:53.657481image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2026-01-14T00:58:55.089574image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2026-01-14T00:58:56.523031image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2026-01-14T00:58:57.940424image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2026-01-14T00:58:59.344939image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2026-01-14T00:59:00.778997image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2026-01-14T00:59:02.225948image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2026-01-14T00:59:03.731756image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2026-01-14T00:58:49.601917image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2026-01-14T00:58:51.005828image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2026-01-14T00:58:52.411842image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2026-01-14T00:58:53.794872image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2026-01-14T00:58:55.226602image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2026-01-14T00:58:56.663483image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2026-01-14T00:58:58.087266image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2026-01-14T00:58:59.482001image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2026-01-14T00:59:00.921344image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2026-01-14T00:59:02.352795image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2026-01-14T00:59:03.858131image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2026-01-14T00:58:49.724746image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2026-01-14T00:58:51.130255image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2026-01-14T00:58:52.538405image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2026-01-14T00:58:53.938974image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2026-01-14T00:58:55.361490image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2026-01-14T00:58:56.793968image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2026-01-14T00:58:58.217580image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2026-01-14T00:58:59.610514image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2026-01-14T00:59:01.053231image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2026-01-14T00:59:02.482601image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2026-01-14T00:59:03.994400image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2026-01-14T00:58:49.846336image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2026-01-14T00:58:51.257285image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2026-01-14T00:58:52.660366image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2026-01-14T00:58:54.065731image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2026-01-14T00:58:55.488617image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2026-01-14T00:58:56.913832image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2026-01-14T00:58:58.341162image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2026-01-14T00:58:59.744738image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2026-01-14T00:59:01.180825image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2026-01-14T00:59:02.602184image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2026-01-14T00:59:04.120279image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2026-01-14T00:58:49.978358image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2026-01-14T00:58:51.383131image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2026-01-14T00:58:52.780203image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2026-01-14T00:58:54.193731image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2026-01-14T00:58:55.622679image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2026-01-14T00:58:57.040028image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2026-01-14T00:58:58.458628image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2026-01-14T00:58:59.867679image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2026-01-14T00:59:01.313209image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2026-01-14T00:59:02.722209image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2026-01-14T00:59:04.245222image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2026-01-14T00:58:50.127694image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2026-01-14T00:58:51.520920image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2026-01-14T00:58:52.903763image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2026-01-14T00:58:54.323469image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2026-01-14T00:58:55.748214image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2026-01-14T00:58:57.172282image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2026-01-14T00:58:58.586491image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2026-01-14T00:58:59.995516image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2026-01-14T00:59:01.455687image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2026-01-14T00:59:02.846858image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2026-01-14T00:59:04.376107image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2026-01-14T00:58:50.260016image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2026-01-14T00:58:51.656797image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2026-01-14T00:58:53.033112image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2026-01-14T00:58:54.460634image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2026-01-14T00:58:55.883495image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2026-01-14T00:58:57.310573image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2026-01-14T00:58:58.721623image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2026-01-14T00:59:00.132331image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2026-01-14T00:59:01.592735image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2026-01-14T00:59:02.980332image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2026-01-14T00:59:04.493107image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2026-01-14T00:58:50.371407image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2026-01-14T00:58:51.776684image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2026-01-14T00:58:53.156605image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2026-01-14T00:58:54.580322image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2026-01-14T00:58:56.004808image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2026-01-14T00:58:57.433192image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2026-01-14T00:58:58.841133image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2026-01-14T00:59:00.250493image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2026-01-14T00:59:01.717967image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2026-01-14T00:59:03.105409image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2026-01-14T00:59:11.516520image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
brandcpu_corescpu_threadsdisplay_sizegpu_brandgpu_tiergpu_vramos_familypriceproc_brandproc_genproc_tierramram_typeresolution_heightresolution_widthromrom_typespec_ratingwarranty
brand1.0000.1550.1350.1810.2200.1940.1410.1340.0930.2410.1410.1100.1240.2800.1500.1540.1300.0970.0890.391
cpu_cores0.1551.0000.8790.2350.4020.3720.3710.0840.7690.5530.6890.5650.5820.2570.5020.4310.4550.1400.3060.088
cpu_threads0.1350.8791.0000.3290.3040.3020.5520.1610.8170.2580.3800.5950.6620.2360.5480.4800.4910.2390.4130.094
display_size0.1810.2350.3291.0000.2640.2270.5270.1750.2670.0680.0980.1910.1760.2070.1190.1760.2640.1970.3500.091
gpu_brand0.2200.4020.3040.2641.0000.7950.5470.0860.2970.8010.5550.5340.2050.3310.1200.1910.1860.0460.3020.170
gpu_tier0.1940.3720.3020.2270.7951.0000.6810.0730.2700.7510.4240.4530.2240.3100.1200.1730.1820.0860.2820.131
gpu_vram0.1410.3710.5520.5270.5470.6811.0000.0000.5680.0990.1090.3120.3790.3010.2050.2890.3460.0580.4930.129
os_family0.1340.0840.1610.1750.0860.0730.0001.0000.0100.0690.3420.2590.1140.2070.1800.1910.3840.2710.0000.333
price0.0930.7690.8170.2670.2970.2700.5680.0101.0000.1340.4900.3560.7200.2250.5980.4930.5580.0650.4330.101
proc_brand0.2410.5530.2580.0680.8010.7510.0990.0690.1341.0000.9780.9780.0190.1230.0900.0860.1160.0660.0000.161
proc_gen0.1410.6890.3800.0980.5550.4240.1090.3420.4900.9781.0000.5290.3330.2860.3560.2940.3470.2270.1370.223
proc_tier0.1100.5650.5950.1910.5340.4530.3120.2590.3560.9780.5291.0000.4990.2180.2780.2580.4480.1690.2730.128
ram0.1240.5820.6620.1760.2050.2240.3790.1140.7200.0190.3330.4991.0000.2610.4850.4200.5000.0890.2790.051
ram_type0.2800.2570.2360.2070.3310.3100.3010.2070.2250.1230.2860.2180.2611.0000.1910.2020.2510.1100.2360.113
resolution_height0.1500.5020.5480.1190.1200.1200.2050.1800.5980.0900.3560.2780.4850.1911.0000.6880.3770.0920.2820.096
resolution_width0.1540.4310.4800.1760.1910.1730.2890.1910.4930.0860.2940.2580.4200.2020.6881.0000.4100.0320.2590.071
rom0.1300.4550.4910.2640.1860.1820.3460.3840.5580.1160.3470.4480.5000.2510.3770.4101.0000.3340.2750.049
rom_type0.0970.1400.2390.1970.0460.0860.0580.2710.0650.0660.2270.1690.0890.1100.0920.0320.3341.0000.0000.000
spec_rating0.0890.3060.4130.3500.3020.2820.4930.0000.4330.0000.1370.2730.2790.2360.2820.2590.2750.0001.0000.090
warranty0.3910.0880.0940.0910.1700.1310.1290.3330.1010.1610.2230.1280.0510.1130.0960.0710.0490.0000.0901.000

Missing values

2026-01-14T00:59:04.687993image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2026-01-14T00:59:05.037586image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

brandpricespec_ratingramram_typeromrom_typedisplay_sizeresolution_widthresolution_heightwarrantyproc_brandproc_tierproc_gengpu_brandgpu_tiergpu_vramcpu_corescpu_threadsos_family
0HP4990073.008DDR4512SSD15.61920.01080.01AMDR55.0AMDOther46.012.0Windows
1HP3990060.008DDR4512SSD15.61920.01080.01Inteli312.0IntelIntegrated06.08.0Windows
2Acer2699069.328DDR4512SSD14.01920.01080.01Inteli311.0IntelIntegrated02.04.0Windows
3Lenovo5972966.0016LPDDR5512SSD14.02240.01400.01Inteli512.0IntelIntegrated012.016.0Windows
5Acer3999062.008DDR4512SSD14.01920.01080.01Inteli512.0IntelIntegrated012.016.0Windows
6Dell3679060.008DDR4512SSD15.61920.01080.01Inteli312.0IntelIntegrated06.08.0Windows
7Acer7699063.0016DDR5512SSD15.61920.01080.01Inteli513.0NVIDIARTX68.012.0Windows
8Asus4899064.008DDR4512SSD15.61920.01080.01Inteli512.0IntelIntegrated012.016.0Windows
9Samsung7499068.0016LPDDR5512SSD13.31080.01920.01Inteli512.0IntelIntegrated012.016.0Windows
10Lenovo4999069.3216DDR4512SSD15.61920.01080.01Inteli712.0IntelIntegrated010.012.0Windows
brandpricespec_ratingramram_typeromrom_typedisplay_sizeresolution_widthresolution_heightwarrantyproc_brandproc_tierproc_gengpu_brandgpu_tiergpu_vramcpu_corescpu_threadsos_family
880HP5999064.008DDR4512SSD15.61920.01080.01AMDR55.0NVIDIARTX46.012.0Windows
881Acer10739969.3216LPDDR51024SSD16.03840.02400.01AMDR77.0AMDOther08.016.0Windows
884Dell18749079.0016DDR51024SSD16.02560.01600.01Inteli913.0NVIDIARTX824.032.0Windows
886Acer4999069.3216LPDDR4X512SSD14.01920.01080.01Inteli313.0IntelIntegrated06.08.0Windows
887Acer5699069.3216LPDDR5512SSD15.61920.01080.01Inteli513.0IntelIntegrated010.012.0Windows
888Asus4499069.328DDR4512SSD15.61920.01080.01Inteli313.0IntelIntegrated06.08.0Windows
889Asus11000071.0016DDR31024SSD15.62560.01440.01AMDR76.0NVIDIARTX68.016.0Windows
890Asus18999089.0032DDR51024SSD14.02560.01600.01AMDR97.0NVIDIARTX88.016.0Windows
891Asus12999073.0016DDR4512SSD15.61920.01080.01Inteli713.0NVIDIARTX614.020.0Windows
892Asus13199084.0016DDR41024SSD15.61920.01080.01AMDR97.0NVIDIARTX68.016.0Windows

Duplicate rows

Most frequently occurring

brandpricespec_ratingramram_typeromrom_typedisplay_sizeresolution_widthresolution_heightwarrantyproc_brandproc_tierproc_gengpu_brandgpu_tiergpu_vramcpu_corescpu_threadsos_family# duplicates
0Asus3399069.328LPDDR5512SSD14.01920.01080.01AMDR37.0AMDOther04.08.0Windows2
1HP3799069.328DDR4512SSD15.61920.01080.01Inteli312.0IntelIntegrated06.08.0Windows2
2Lenovo3299069.328LPDDR5512SSD15.61920.01080.01AMDR37.0AMDOther04.08.0Windows2
3Lenovo3399069.328DDR4256SSD15.61920.01080.01Inteli312.0IntelIntegrated06.08.0Windows2